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    <h2>Welcome to SimGenex</h2>
    <p>Gene expression measurements are measurements that determine the amount of product of one or more genes in a biological sample. The amount or concentration of a gene product is called the expression level of the gene that encodes the product. Samples for gene expression measurement are typically cultivated at controlled conditions. While the exact conditions depend on the object of research and the specific research question, the properties that are subject to control can generally be classified into genetic properties and environmental conditions. The set of expression levels of a given gene, measured in different samples, is called the expression profile (or profile, for short) of that gene. The set of expression levels of all genes in all samples is called an expression set, or, in recognition of the “genes × conditions” format of the set, an expression matrix. Genetic properties pertain to the genetic makeup of the subjects. Specifically, genes may be knocked out (loss of function mutations), or they may be overexpressed (gain of function mutations). There is a wide range of environmental conditions that biological subjects may be exposed to. A frequent condition is treatment with some agent, such as a hormone, drug, or other effector. Gene expression levels that have been measured are subjected to various mathematical operations. It is common practice to work in the logarithmic domain (i.e. to take the logarithm of the raw expression levels), because upand down-regulation can be directly compared with such “logarithmised” values. Gene expression measurement can sometimes produce negative values as an artifact. This must be addressed before values are transformed to the logarithmic domain. Adding a small offset is a simple remedy of this problem. Once gene expression levels are adequately conditioned, expression profiles can be compared. Quantitatively, comparison takes place by defining a distance measure that quantifies how dissimilar two profiles are. Two straightforward distance measures are the Euclidean distance and the correlation distance (which is a semi-metric distance), defined as 1 − r(g1, g2), where r(g1, g2) denotes correlation coefficient between the expression profiles of genes g1 and g2. The sum of distances of expression profiles is a distance between two expression sets. The transsys framework provides a basis for simulating regulatory networks with different genetic properties, and for deriving loss or gain of function variants of a given regulatory network by removing or adding genes, respectively. Different environmental conditions can be simulated by designating factors that are subject to external alteration, and using different settings of the the expression levels of these factors to simulate different conditions. The language defined here is designed to enable succinct and flexible specification of such biological processes and experimental procedures in silico that result in a simulated expressionmatrix, and also to specify a distancemeasure to compare the simulated matrix to an target matrix comprised of expression data that is externally provided (i.e. not generated by way of simulation). The target matrix is also called the empirical matrix. In addition to this specification, a transsys program, called the candidate program, that models the regulatory network is required to carry out the simulation. Candidate programs must satisfy certain criteria in order to be suitable for simulation according to a specification. Specifically, the transsys program needs to have factors and genes that are specified by name in the simulation protocol specification. Within these requirements, candidate programs can be freely chosen. 
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    <p><a href="{% url sgx.views.addSgx%}" title="Add a new SimGenex">Click here</a> to create new SimGenex program</p>
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